
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
    from ...tokenization_utils_base import TextInput
from ...utils import logging


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	str       			=  {'vocab_file': 'spiece.model'}

__lowerCAmelCase     :	List[Any]       			=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
__lowerCAmelCase     :	Tuple       			=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

__lowerCAmelCase     :	List[Any]       			=  '▁'




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						PRETRAINED_VOCAB_FILES_MAP
  a__       			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						["""input_ids""", """attention_mask"""]
  def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Dict="</s>"     ,				UpperCamelCase__     :					Any="<unk>"     ,				UpperCamelCase__     :					Any="<pad>"     ,				UpperCamelCase__     :					List[str]=100     ,				UpperCamelCase__     :					Optional[Any]=None     ,				UpperCamelCase__     :					Optional[Dict[str, Any]] = None     ,				UpperCamelCase__     :					Union[str, Any]=True     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      None:







    """simple docstring"""






    if extra_ids > 0 and additional_special_tokens is None:
      __magic_name__								=			[F'''<extra_id_{i}>''' for i in range(UpperCamelCase__       )]
    elif extra_ids > 0 and additional_special_tokens is not None:
      # Check that we have the right number of extra_id special tokens
      __magic_name__								=			len(set(filter(lambda UpperCamelCase__       : bool("""extra_id""" in str(UpperCamelCase__       )       )     ,				UpperCamelCase__       )       )       )
      if extra_tokens != extra_ids:
        raise ValueError(
            F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
            """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
            """ tokens"""       )
    if legacy:
      logger.warning_once(
          F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
          """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""       )

    __magic_name__								=			legacy
    __magic_name__								=			{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        eos_token=UpperCamelCase__     ,				unk_token=UpperCamelCase__     ,				pad_token=UpperCamelCase__     ,				extra_ids=UpperCamelCase__     ,				additional_special_tokens=UpperCamelCase__     ,				sp_model_kwargs=self.sp_model_kwargs     ,				legacy=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

    __magic_name__								=			vocab_file
    __magic_name__								=			extra_ids

    __magic_name__								=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
    self.sp_model.Load(UpperCamelCase__       )
  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					int     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					int       )     ->      str:







    """simple docstring"""






    if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
      __magic_name__								=			TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
      if init_max_model_length is not None and init_max_model_length != max_model_length:
        return init_max_model_length
      elif init_max_model_length is None:
        warnings.warn(
            """This tokenizer was incorrectly instantiated with a model max length of"""
            F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
            """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
            """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
            F''' {pretrained_model_name_or_path} automatically truncating your input to'''
            F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
            F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
            """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
            """ instantiate this tokenizer with `model_max_length` set to your preferred value."""     ,				UpperCamelCase__     ,				)

    return max_model_length
  @property
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    return self.sp_model.get_piece_size() + self._extra_ids
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			{self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
    vocab.update(self.added_tokens_encoder       )
    return vocab
  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None     ,				UpperCamelCase__     :					bool = False       )     ->      List[int]:







    """simple docstring"""






    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=UpperCamelCase__     ,				token_ids_a=UpperCamelCase__     ,				already_has_special_tokens=UpperCamelCase__       )

    # normal case: some special tokens
    if token_ids_a is None:
      return ([0] * len(UpperCamelCase__       )) + [1]
    return ([0] * len(UpperCamelCase__       )) + [1] + ([0] * len(UpperCamelCase__       )) + [1]
  def 				_lowercase				(					self     :					Dict       )     ->      Optional[Any]:







    """simple docstring"""






    return list(
        set(filter(lambda UpperCamelCase__       : bool(re.search(R"""<extra_id_\d+>"""     ,				UpperCamelCase__       )       ) is not None     ,				self.additional_special_tokens       )       )       )
  def 				_lowercase				(					self     :					Dict       )     ->      List[Any]:







    """simple docstring"""






    return [self._convert_token_to_id(UpperCamelCase__       ) for token in self.get_sentinel_tokens()]
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					List[int]       )     ->      List[int]:







    """simple docstring"""






    if len(UpperCamelCase__       ) > 0 and token_ids[-1] == self.eos_token_id:
      warnings.warn(
          F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
          """ eos tokens being added."""       )
      return token_ids
    else:
      return token_ids + [self.eos_token_id]
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None       )     ->      List[int]:







    """simple docstring"""






    __magic_name__								=			[self.eos_token_id]

    if token_ids_a is None:
      return len(token_ids_a + eos       ) * [0]
    return len(token_ids_a + eos + token_ids_a + eos       ) * [0]
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None       )     ->      List[int]:







    """simple docstring"""






    __magic_name__								=			self._add_eos_if_not_present(UpperCamelCase__       )
    if token_ids_a is None:
      return token_ids_a
    else:
      __magic_name__								=			self._add_eos_if_not_present(UpperCamelCase__       )
      return token_ids_a + token_ids_a
  def __getstate__(					self     :					Optional[int]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			self.__dict__.copy()
    __magic_name__								=			None
    return state
  def __setstate__(					self     :					int     ,				UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    __magic_name__								=			d

    # for backward compatibility
    if not hasattr(self     ,				"""sp_model_kwargs"""       ):
      __magic_name__								=			{}

    __magic_name__								=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
    self.sp_model.Load(self.vocab_file       )
  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					"TextInput"     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    if not self.legacy:
      __magic_name__								=			SPIECE_UNDERLINE + text.replace(UpperCamelCase__     ,				""" """       )
    return super().tokenize(UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					List[Any]     ,				**UpperCamelCase__     :					str       )     ->      List[Any]:







    """simple docstring"""






    if not self.legacy:
      __magic_name__								=			text.startswith(UpperCamelCase__       )
      if is_first:
        __magic_name__								=			text[1:]

    __magic_name__								=			self.sp_model.encode(UpperCamelCase__     ,				out_type=UpperCamelCase__       )

    if not self.legacy and not is_first and not text.startswith(""" """       ) and tokens[0].startswith(UpperCamelCase__       ):
      __magic_name__								=			([tokens[0][1:]] if len(tokens[0]       ) > 1 else []) + tokens[1:]
    return tokens
  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					Dict       )     ->      List[str]:







    """simple docstring"""






    if token.startswith("""<extra_id_"""       ):
      __magic_name__								=			re.match(R"""<extra_id_(\d+)>"""     ,				UpperCamelCase__       )
      __magic_name__								=			int(match.group(1       )       )
      return self.vocab_size - num - 1
    return self.sp_model.piece_to_id(UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Dict       )     ->      Dict:







    """simple docstring"""






    if index < self.sp_model.get_piece_size():
      __magic_name__								=			self.sp_model.IdToPiece(UpperCamelCase__       )
    else:
      __magic_name__								=			F'''<extra_id_{self.vocab_size - 1 - index}>'''
    return token
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					List[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			""""""
    __magic_name__								=			False
    for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
        if not prev_is_special:
          out_string += " "
        out_string += self.sp_model.decode(UpperCamelCase__       ) + token
        __magic_name__								=			True
        __magic_name__								=			[]
      else:
        current_sub_tokens.append(UpperCamelCase__       )
        __magic_name__								=			False
    out_string += self.sp_model.decode(UpperCamelCase__       )
    return out_string.strip()





  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[str] = None       )     ->      Tuple[str]:







    """simple docstring"""






    if not os.path.isdir(UpperCamelCase__       ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
      return
    __magic_name__								=			os.path.join(
        UpperCamelCase__     ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

    if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCamelCase__       ) and os.path.isfile(self.vocab_file       ):
      copyfile(self.vocab_file     ,				UpperCamelCase__       )
    elif not os.path.isfile(self.vocab_file       ):
      with open(UpperCamelCase__     ,				"""wb"""       ) as fi:
        __magic_name__								=			self.sp_model.serialized_model_proto()
        fi.write(UpperCamelCase__       )

    return (out_vocab_file,)



import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]=0.01     ,				UpperCamelCase__     :					Tuple=1000       )     ->      str:







    """simple docstring"""






    __magic_name__								=			p_stop
    __magic_name__								=			max_length





  def __iter__(					self     :					Union[str, Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			0
    __magic_name__								=			False
    while not stop and count < self.max_length:
      yield count
      count += 1
      __magic_name__								=			random.random() < self.p_stop







class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					Optional[Any]=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			[
        BatchSamplerShard(UpperCamelCase__     ,				2     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
        for i in range(2       )
    ]
    __magic_name__								=			[list(UpperCamelCase__       ) for batch_sampler_shard in batch_sampler_shards]
    if not split_batches:
      self.assertListEqual([len(UpperCamelCase__       ) for shard in batch_sampler_shards]     ,				[len(UpperCamelCase__       ) for e in expected]       )
    self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is a round multiple of batch size but not total batch size.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1, 0]], [[1, 0, 1]]]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Tuple       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size or num_processes.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], [[0, 1]]]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					int       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is a round multiple of batch size but not total batch size.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size or num_processes.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
    __magic_name__								=			[BatchSamplerShard(UpperCamelCase__     ,				2     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       ) for i in range(2       )]

    self.assertEqual(len(batch_sampler_shards[0]       )     ,				3       )
    self.assertEqual(len(batch_sampler_shards[1]       )     ,				2       )

    self.assertListEqual(list(batch_sampler_shards[0]       )     ,				[[0, 1, 2], [5, 6, 7, 8], [12, 13]]       )
    self.assertListEqual(list(batch_sampler_shards[1]       )     ,				[[3, 4], [9, 10, 11]]       )
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					Union[str, Any]=2     ,				UpperCamelCase__     :					Union[str, Any]=False       )     ->      Union[str, Any]:







    """simple docstring"""






    random.seed(UpperCamelCase__       )
    __magic_name__								=			list(UpperCamelCase__       )

    __magic_name__								=			[
        IterableDatasetShard(
            UpperCamelCase__     ,				batch_size=UpperCamelCase__     ,				drop_last=UpperCamelCase__     ,				num_processes=UpperCamelCase__     ,				process_index=UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				)
        for i in range(UpperCamelCase__       )
    ]
    __magic_name__								=			[]
    for iterable_dataset_shard in iterable_dataset_shards:
      # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
      random.seed(UpperCamelCase__       )
      iterable_dataset_lists.append(list(UpperCamelCase__       )       )

    __magic_name__								=			batch_size // num_processes if split_batches else batch_size
    # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
    __magic_name__								=			iterable_dataset_lists[0]
    for l in iterable_dataset_lists[1:]:
      self.assertEqual(len(UpperCamelCase__       )     ,				len(UpperCamelCase__       )       )
      self.assertTrue(len(UpperCamelCase__       ) % shard_batch_size == 0       )

    __magic_name__								=			[]
    for idx in range(0     ,				len(UpperCamelCase__       )     ,				UpperCamelCase__       ):
      for l in iterable_dataset_lists:
        observed += l[idx : idx + shard_batch_size]

    if not drop_last:
      while len(UpperCamelCase__       ) < len(UpperCamelCase__       ):
        reference += reference
    self.assertListEqual(UpperCamelCase__     ,				reference[: len(UpperCamelCase__       )]       )
  def 				_lowercase				(					self     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			42
    __magic_name__								=			RandomIterableDataset()

    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Edge case with a very small dataset
    __magic_name__								=			RandomIterableDataset(max_length=2       )

    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(16       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			SkipBatchSampler(UpperCamelCase__     ,				2       )
    self.assertListEqual(list(UpperCamelCase__       )     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					Tuple       )     ->      str:







    """simple docstring"""






    __magic_name__								=			SkipDataLoader(list(range(16       )       )     ,				batch_size=4     ,				skip_batches=2       )
    self.assertListEqual([t.tolist() for t in dataloader]     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					str       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			DataLoader(list(range(16       )       )     ,				batch_size=4       )
    __magic_name__								=			skip_first_batches(UpperCamelCase__     ,				num_batches=2       )
    self.assertListEqual([t.tolist() for t in new_dataloader]     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			DataLoaderShard(list(range(16       )       )     ,				batch_size=4       )
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

    # Test it also works on the second iteration
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )





  def 				_lowercase				(					self     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    Accelerator()
    __magic_name__								=			DataLoaderDispatcher(range(16       )     ,				batch_size=4       )
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

    # Test it also works on the second iteration
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

